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:book: VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud (CVPR 2023 Highlight)

Introduction

This is a release of the code of our paper VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud (CVPR 2023 Highlight).

Authors: Ziqin Wang, Bowen Cheng, Lichen Zhao, Dong Xu, Yang Tang, Lu Sheng* (*corresponding author)

[arxiv] [code] [checkpoint]

Dependencies

conda create -n vlsat python=3.8
conda activate vlsat
pip install -r requirement.txt
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.1+cu113.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.12.1+cu113.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.12.1+cu113.html
pip install torch-geometric
pip install git+https://github.com/openai/CLIP.git

Prepare the data

A. Download 3Rscan and 3DSSG-Sub Annotation, you can follow 3DSSG

B. Generate 2D Multi View Image

# you should motify the path in pointcloud2image.py into your own path
python data/pointcloud2image.py

C. You should arrange the file location like this

data
  3DSSG_subset
    relations.txt
    classes.txt
    
  3RScan
    0a4b8ef6-a83a-21f2-8672-dce34dd0d7ca
      multi_view
      labels.instances.align.annotated.v2.ply
    ...  
      

D. Train your own clip adapter

python clip_adapter/main.py

or just use the checkpoint

clip_adapter/checkpoint/origin_mean.pth

Run Code

# Train
python -m main --mode train --config <config_path> --exp <exp_name>
# Eval
python -m main --mode eval --config <config_path> --exp <exp_name>

In this repo, we have provided a default config

Paper

If you find the code useful please consider citing our paper:

@article{wang2023vl,
  title={VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud},
  author={Wang, Ziqin and Cheng, Bowen and Zhao, Lichen and Xu, Dong and Tang, Yang and Sheng, Lu},
  journal={arXiv preprint arXiv:2303.14408},
  year={2023}
}

Acknowledgement

This repository is partly based on 3DSSG and CLIP repositories.